shaman-ai / agent-actors Goto Github PK

FAILED agent_actors/test_system.py::TestSystem::test_parent_overhead - ray.exceptions.RayTaskError(TypeError): ray::AgentActor.run() (pid=23184, ip=127.0.0.1, repr=<agent_actors.actors.AgentActor object at 0x0000025CF0E45660>)

self = <agent_actors.test_system.TestSystem object at 0x000002E43B6D36A0>

    def test_parent_overhead(self):
        luke = self.create_parent(
            name="Luke",
            traits=["project manager", "golden retriever energy"],
            max_iterations=3,
        )
        amir = self.create_child(
            name="Amir",
            traits=["smart", "kind"],
            max_iterations=3,
        )
        luke.children = {0: amir}
>       luke.run(task="What is Sergey Brin's age multiplied by 12?")

agent_actors\test_system.py:100:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
agent_actors\parent.py:105: in run
    results = ray.get(tasks_completed)
C:\Users\finbi\anaconda3\envs\py3912\lib\site-packages\ray\_private\client_mode_hook.py:105: in wrapper
    return func(*args, **kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

object_refs = [ObjectRef(32d950ec0ccf9d2a5f96a25e5338223a08ab16470100000001000000), ObjectRef(e0dc174c835990345f96a25e5338223a08ab16470100000001000000), ObjectRef(f4402ec78d3a26075f96a25e5338223a08ab16470100000001000000)]

    @PublicAPI
    @client_mode_hook(auto_init=True)
    def get(
        object_refs: Union[ray.ObjectRef, Sequence[ray.ObjectRef]],
        *,
        timeout: Optional[float] = None,
    ) -> Union[Any, List[Any]]:
        """Get a remote object or a list of remote objects from the object store.

        This method blocks until the object corresponding to the object ref is
        available in the local object store. If this object is not in the local
        object store, it will be shipped from an object store that has it (once the
        object has been created). If object_refs is a list, then the objects
        corresponding to each object in the list will be returned.

        Ordering for an input list of object refs is preserved for each object
        returned. That is, if an object ref to A precedes an object ref to B in the
        input list, then A will precede B in the returned list.

        This method will issue a warning if it's running inside async context,
        you can use ``await object_ref`` instead of ``ray.get(object_ref)``. For
        a list of object refs, you can use ``await asyncio.gather(*object_refs)``.

        Related patterns and anti-patterns:

        - :doc:`/ray-core/patterns/ray-get-loop`
        - :doc:`/ray-core/patterns/unnecessary-ray-get`
        - :doc:`/ray-core/patterns/ray-get-submission-order`
        - :doc:`/ray-core/patterns/ray-get-too-many-objects`


        Args:
            object_refs: Object ref of the object to get or a list of object refs
                to get.
            timeout (Optional[float]): The maximum amount of time in seconds to
                wait before returning. Set this to None will block until the
                corresponding object becomes available.
                WARNING: In future ray releases ``timeout=0`` will return the object
                immediately if it's available, else raise GetTimeoutError in accordance with
                the above docstring. The current behavior of blocking until objects become
                available of ``timeout=0`` is considered to be a bug, see
                https://github.com/ray-project/ray/issues/28465.

        Returns:
            A Python object or a list of Python objects.

        Raises:
            GetTimeoutError: A GetTimeoutError is raised if a timeout is set and
                the get takes longer than timeout to return.
            Exception: An exception is raised if the task that created the object
                or that created one of the objects raised an exception.
        """
        if timeout == 0:
            if os.environ.get("RAY_WARN_RAY_GET_TIMEOUT_ZERO", "1") == "1":
                import warnings

                warnings.warn(
                    (
                        "Please use timeout=None if you expect ray.get() to block. "
                        "Setting timeout=0 in future ray releases will raise "
                        "GetTimeoutError if the objects references are not available. "
                        "You could suppress this warning by setting "
                        "RAY_WARN_RAY_GET_TIMEOUT_ZERO=0."
                    ),
                    UserWarning,
                )

            # Record this usage in telemetry
            import ray._private.usage.usage_lib as usage_lib

            usage_lib.record_extra_usage_tag(usage_lib.TagKey.RAY_GET_TIMEOUT_ZERO, "True")

        worker = global_worker
        worker.check_connected()

        if hasattr(worker, "core_worker") and worker.core_worker.current_actor_is_asyncio():
            global blocking_get_inside_async_warned
            if not blocking_get_inside_async_warned:
                logger.warning(
                    "Using blocking ray.get inside async actor. "
                    "This blocks the event loop. Please use `await` "
                    "on object ref with asyncio.gather if you want to "
                    "yield execution to the event loop instead."
                )
                blocking_get_inside_async_warned = True

        with profiling.profile("ray.get"):
            is_individual_id = isinstance(object_refs, ray.ObjectRef)
            if is_individual_id:
                object_refs = [object_refs]

            if not isinstance(object_refs, list):
                raise ValueError(
                    "'object_refs' must either be an ObjectRef or a list of ObjectRefs."
                )

            # TODO(ujvl): Consider how to allow user to retrieve the ready objects.
            values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)
            for i, value in enumerate(values):
                if isinstance(value, RayError):
                    if isinstance(value, ray.exceptions.ObjectLostError):
                        worker.core_worker.dump_object_store_memory_usage()
                    if isinstance(value, RayTaskError):
>                       raise value.as_instanceof_cause()
E                       ray.exceptions.RayTaskError(TypeError): ray::AgentActor.run() (pid=23184, ip=127.0.0.1, repr=<agent_actors.actors.AgentActor object at 0x0000025CF0E45660>)
E                         File "python\ray\_raylet.pyx", line 857, in ray._raylet.execute_task
E                         File "python\ray\_raylet.pyx", line 861, in ray._raylet.execute_task
E                         File "python\ray\_raylet.pyx", line 803, in ray._raylet.execute_task.function_executor
E                         File "C:\Users\finbi\anaconda3\envs\py3912\lib\site-packages\ray\_private\function_manager.py", line 674, in actor_method_executor
E                           return method(__ray_actor, *args, **kwargs)
E                         File "C:\Users\finbi\anaconda3\envs\py3912\lib\site-packages\ray\util\tracing\tracing_helper.py", line 466, in _resume_span
E                           return method(self, *_args, **_kwargs)
E                         File "C:\Users\finbi\pymaindir\aagi\Agents\la_multiagents\agent-actors\agent_actors\actors.py", line 12, in run
E                           return self.agent.run(*args, **kwargs)
E                         File "C:\Users\finbi\pymaindir\aagi\Agents\la_multiagents\agent-actors\agent_actors\child.py", line 49, in run
E                           self.add_memory(learning)
E                         File "C:\Users\finbi\pymaindir\aagi\Agents\la_multiagents\agent-actors\agent_actors\agent.py", line 150, in add_memory
E                           self.pause_to_reflect()
E                         File "C:\Users\finbi\pymaindir\aagi\Agents\la_multiagents\agent-actors\agent_actors\agent.py", line 240, in pause_to_reflect
E                           insights = self._pause_to_reflect()
E                         File "C:\Users\finbi\pymaindir\aagi\Agents\la_multiagents\agent-actors\agent_actors\agent.py", line 247, in _pause_to_reflect
E                           new_insights = self._synthesize_memories()
E                         File "C:\Users\finbi\pymaindir\aagi\Agents\la_multiagents\agent-actors\agent_actors\agent.py", line 207, in _synthesize_memories
E                           return self.synthesis(
E                       TypeError: Chain.__call__() got an unexpected keyword argument 'context'

C:\Users\finbi\anaconda3\envs\py3912\lib\site-packages\ray\_private\worker.py:2380: RayTaskError(TypeError)

from agent_actors.callback_manager import ConsolePrettyPrintManager

cd agent-actors

poetry add git+https://github.com/shaman-ai/agent-actors.git

Package 'agent-actors' is listed as a dependency of itself.

poetry install --with dev --with typing

Loading .env environment variables...
Installing git+https://github.com/shaman-ai/agent-actors.git#egg=agent-actors...
Resolving git+https://github.com/shaman-ai/agent-actors.git#egg=agent-actors...
Installing...
[    ] Installing agent-actors...[31m[1mError: [0m An error occurred while installing [32mgit+https://github.com/shaman-ai/agent-actors.git#egg=agent-actors[0m!
Error text: Collecting agent-actors (from -r c:\users\finbi\appdata\local\temp\pipenv-mb2k0hrk-requirements\pipenv-jpnwji4g-requirement.txt (line 1))
  Cloning https://github.com/shaman-ai/agent-actors.git to c:\users\finbi\appdata\local\temp\pip-install-oms8w0i0\agent-actors_2edcb81a7c2a4d6988dd6c92554d11e1
  Resolved https://github.com/shaman-ai/agent-actors.git to commit 9bb3690a8adeff3501ceef10b3659046376e2ac2
  Installing build dependencies: started
  Installing build dependencies: finished with status 'done'
  Getting requirements to build wheel: started
  Getting requirements to build wheel: finished with status 'done'
  Preparing metadata (pyproject.toml): started
  Preparing metadata (pyproject.toml): finished with status 'done'
Collecting langchain@ git+https://github.com/hwchase17/langchain.git@master (from agent-actors->-r c:\users\finbi\appdata\local\temp\pipenv-mb2k0hrk-requirements\pipenv-jpnwji4g-requirement.txt
(line 1))
  Cloning https://github.com/hwchase17/langchain.git (to revision master) to c:\users\finbi\appdata\local\temp\pip-install-oms8w0i0\langchain_64424163946e4fb39dff885c90088326
  Resolved https://github.com/hwchase17/langchain.git to commit 3bd5a99b835fa320d02aa733cb0c0bc4a87724fa
  Installing build dependencies: started
  Installing build dependencies: finished with status 'done'
  Getting requirements to build wheel: started
  Getting requirements to build wheel: finished with status 'done'
  Preparing metadata (pyproject.toml): started
  Preparing metadata (pyproject.toml): finished with status 'done'
Collecting aiohttp<4.0.0,>=3.8.4 (from agent-actors->-r c:\users\finbi\appdata\local\temp\pipenv-mb2k0hrk-requirements\pipenv-jpnwji4g-requirement.txt (line 1))
  Using cached aiohttp-3.8.4-cp39-cp39-win_amd64.whl (323 kB)
Collecting gptcache<0.2.0,>=0.1.17 (from agent-actors->-r c:\users\finbi\appdata\local\temp\pipenv-mb2k0hrk-requirements\pipenv-jpnwji4g-requirement.txt (line 1))
  Using cached gptcache-0.1.21-py3-none-any.whl (79 kB)
Collecting openai<0.28.0,>=0.27.4 (from agent-actors->-r c:\users\finbi\appdata\local\temp\pipenv-mb2k0hrk-requirements\pipenv-jpnwji4g-requirement.txt (line 1))
  Downloading openai-0.27.6-py3-none-any.whl (71 kB)
     ---------------------------------------- 71.9/71.9 kB 1.9 MB/s eta 0:00:00
Collecting pydantic<2.0.0,>=1.10.7 (from agent-actors->-r c:\users\finbi\appdata\local\temp\pipenv-mb2k0hrk-requirements\pipenv-jpnwji4g-requirement.txt (line 1))
  Using cached pydantic-1.10.7-cp39-cp39-win_amd64.whl (2.2 MB)
Collecting python-dotenv<2.0.0,>=1.0.0 (from agent-actors->-r c:\users\finbi\appdata\local\temp\pipenv-mb2k0hrk-requirements\pipenv-jpnwji4g-requirement.txt (line 1))
  Using cached python_dotenv-1.0.0-py3-none-any.whl (19 kB)
Collecting ray<3.0.0,>=2.3.1 (from agent-actors->-r c:\users\finbi\appdata\local\temp\pipenv-mb2k0hrk-requirements\pipenv-jpnwji4g-requirement.txt (line 1))
  Using cached ray-2.4.0-cp39-cp39-win_amd64.whl (21.7 MB)
Collecting termcolor<3.0.0,>=2.2.0 (from agent-actors->-r c:\users\finbi\appdata\local\temp\pipenv-mb2k0hrk-requirements\pipenv-jpnwji4g-requirement.txt (line 1))
  Using cached termcolor-2.3.0-py3-none-any.whl (6.9 kB)
Collecting tiktoken<0.4.0,>=0.3.3 (from agent-actors->-r c:\users\finbi\appdata\local\temp\pipenv-mb2k0hrk-requirements\pipenv-jpnwji4g-requirement.txt (line 1))
  Using cached tiktoken-0.3.3-cp39-cp39-win_amd64.whl (579 kB)
Collecting wikipedia<2.0.0,>=1.4.0 (from agent-actors->-r c:\users\finbi\appdata\local\temp\pipenv-mb2k0hrk-requirements\pipenv-jpnwji4g-requirement.txt (line 1))
  Using cached wikipedia-1.4.0.tar.gz (27 kB)
  Preparing metadata (setup.py): started
  Preparing metadata (setup.py): finished with status 'done'
Collecting wolframalpha<6.0.0,>=5.0.0 (from agent-actors->-r c:\users\finbi\appdata\local\temp\pipenv-mb2k0hrk-requirements\pipenv-jpnwji4g-requirement.txt (line 1))
  Using cached wolframalpha-5.0.0-py3-none-any.whl (7.5 kB)
INFO: pip is looking at multiple versions of agent-actors to determine which version is compatible with other requirements. This could take a while.

[   =] Installing agent-actors...[36m  Running command git clone --filter=blob:none --quiet https://github.com/shaman-ai/agent-actors.git
'C:\Users\finbi\AppData\Local\Temp\pip-install-oms8w0i0\agent-actors_2edcb81a7c2a4d6988dd6c92554d11e1'
  Running command git clone --filter=blob:none --quiet https://github.com/hwchase17/langchain.git
'C:\Users\finbi\AppData\Local\Temp\pip-install-oms8w0i0\langchain_64424163946e4fb39dff885c90088326'
ERROR: Package 'agent-actors' requires a different Python: 3.9.12 not in '<4.0.0,>=3.10.9'
[0m
Installation Failed

Resolving dependencies...
✘ Locking Failed!
⠇ Locking...
Traceback (most recent call last):
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/resolver.py", line 845, in <module>
    main()
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/resolver.py", line 831, in main
    _main(
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/resolver.py", line 811, in _main
    resolve_packages(
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/resolver.py", line 759, in resolve_packages
    results, resolver = resolve(
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/resolver.py", line 738, in resolve
    return resolve_deps(
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/utils/resolver.py", line 1096, in resolve_deps
    results, hashes, markers_lookup, resolver, skipped = actually_resolve_deps(
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/utils/resolver.py", line 884, in actually_resolve_deps
    resolver = Resolver.create(
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/utils/resolver.py", line 455, in create
    constraints, skipped, index_lookup, markers_lookup = resolver.get_metadata(
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/utils/resolver.py", line 245, in get_metadata
    constraint_update, lockfile_update = self.get_deps_from_req(
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/utils/resolver.py", line 361, in get_deps_from_req
    new_constraints, new_lock = self.get_deps_from_req(
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/utils/resolver.py", line 367, in get_deps_from_req
    elif r and (not r.marker or (r.marker and r.marker.evaluate())):
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/patched/pip/_vendor/packaging/markers.py", line 304, in evaluate
    return _evaluate_markers(self._markers, current_environment)
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/patched/pip/_vendor/packaging/markers.py", line 229, in _evaluate_markers
    groups[-1].append(_evaluate_markers(marker, environment))
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/patched/pip/_vendor/packaging/markers.py", line 234, in _evaluate_markers
    lhs_value = _get_env(environment, lhs.value)
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/patched/pip/_vendor/packaging/markers.py", line 215, in _get_env
    raise UndefinedEnvironmentName(
pipenv.patched.pip._vendor.packaging.markers.UndefinedEnvironmentName: 'extra' does not exist in evaluation environment.

(py310) luca@luca-UX31E:~/py_conda/agents/agent-actors$ pipenv install git+https://github.com/shaman-ai/agent-actors.git#egg=agent-actors
Installing git+https://github.com/shaman-ai/agent-actors.git#egg=agent-actors...
Resolving git+https://github.com/shaman-ai/agent-actors.git#egg=agent-actors...
Installing...
Adding agent-actors to Pipfile's [packages] ...
✔ Installation Succeeded
Pipfile.lock not found, creating...
Locking [packages] dependencies...
Building requirements...
Resolving dependencies...
✘ Locking Failed!
⠋ Locking...
Traceback (most recent call last):
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/resolver.py", line 845, in <module>
    main()
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/resolver.py", line 831, in main
    _main(
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/resolver.py", line 811, in _main
    resolve_packages(
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/resolver.py", line 759, in resolve_packages
    results, resolver = resolve(
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/resolver.py", line 738, in resolve
    return resolve_deps(
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/utils/resolver.py", line 1096, in resolve_deps
    results, hashes, markers_lookup, resolver, skipped = actually_resolve_deps(
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/utils/resolver.py", line 884, in actually_resolve_deps
    resolver = Resolver.create(
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/utils/resolver.py", line 455, in create
    constraints, skipped, index_lookup, markers_lookup = resolver.get_metadata(
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/utils/resolver.py", line 245, in get_metadata
    constraint_update, lockfile_update = self.get_deps_from_req(
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/utils/resolver.py", line 361, in get_deps_from_req
    new_constraints, new_lock = self.get_deps_from_req(
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/utils/resolver.py", line 367, in get_deps_from_req
    elif r and (not r.marker or (r.marker and r.marker.evaluate())):
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/patched/pip/_vendor/packaging/markers.py", line 304, in evaluate
    return _evaluate_markers(self._markers, current_environment)
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/patched/pip/_vendor/packaging/markers.py", line 229, in _evaluate_markers
    groups[-1].append(_evaluate_markers(marker, environment))
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/patched/pip/_vendor/packaging/markers.py", line 234, in _evaluate_markers
    lhs_value = _get_env(environment, lhs.value)
  File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/pipenv/patched/pip/_vendor/packaging/markers.py", line 215, in _get_env
    raise UndefinedEnvironmentName(
pipenv.patched.pip._vendor.packaging.markers.UndefinedEnvironmentName: 'extra' does not exist in evaluation environment.

  File "/home/ehazar/miniconda3/envs/mma/lib/python3.10/site-packages/agent_actors/parent.py", line 104, in run
    results = ray.get(tasks_completed)
  File "/home/ehazar/miniconda3/envs/mma/lib/python3.10/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
    return func(*args, **kwargs)
  File "/home/ehazar/miniconda3/envs/mma/lib/python3.10/site-packages/ray/_private/worker.py", line 2521, in get
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(KeyError): ray::AgentActor.run() (pid=21229, ip=10.120.210.89, repr=<agent_actors.actors.AgentActor object at 0x7f81e4e6d7b0>)
  File "/home/ehazar/miniconda3/envs/mma/lib/python3.10/site-packages/agent_actors/actors.py", line 12, in run
    return self.agent.run(*args, **kwargs)
  File "/home/ehazar/miniconda3/envs/mma/lib/python3.10/site-packages/agent_actors/parent.py", line 91, in run
    working_memory=[
  File "/home/ehazar/miniconda3/envs/mma/lib/python3.10/site-packages/agent_actors/parent.py", line 92, in <listcomp>
    task_result_refs[d.id] for d in sub_task.dependencies
KeyError: '4.4'

========================================================================= FAILURES ==========================================================================
______________________________________________________________ TestSystem.test_parent_overhead ______________________________________________________________

self = <agent_actors.test_system.TestSystem object at 0x7f165626a770>

    def test_parent_overhead(self):
        luke = self.create_parent(
            name="Luke",
            traits=["project manager", "golden retriever energy"],
            max_iterations=3,
        )
        amir = self.create_child(
            name="Amir",
            traits=["smart", "kind"],
            max_iterations=3,
        )
        luke.children = {0: amir}
>       luke.run(task="What is Sergey Brin's age multiplied by 12?")

agent_actors/test_system.py:100: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
agent_actors/parent.py:109: in run
    self.pause_to_reflect()
agent_actors/agent.py:240: in pause_to_reflect
    insights = self._pause_to_reflect()
agent_actors/agent.py:247: in _pause_to_reflect
    new_insights = self._synthesize_memories()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

self = ParentAgent(name='Luke', traits=['project manager', 'golden retriever energy'], tools=[], task='', children={0: ChildA...penai_organization=None, request_timeout=60, max_retries=6, streaming=False, n=1, max_tokens=1024), output_key='json'))
last_k = 10

    def _synthesize_memories(
        self, last_k: Optional[int] = None
    ) -> Tuple[str, str, str]:
        if last_k is None:
            last_k = self.reflect_every
    
        observations = self.long_term_memory.memory_stream[-last_k:]
        if not any(observations):
            return []
    
>       return self.synthesis(
            context=self.get_header(),
            task=self.task,
            memories="\n".join(o.page_content for o in observations),
        )["items"]
E       TypeError: Chain.__call__() got an unexpected keyword argument 'context'

agent_actors/agent.py:207: TypeError
____________________________________________________________ TestSystem.test_parallel_map_reduce ____________________________________________________________

self = <agent_actors.test_system.TestSystem object at 0x7f1656269030>

    def test_parallel_map_reduce(self):
        jiang = self.create_child(
            name="Jiang",
            traits=["sharp", "math, stats, and data whiz", "capitalist"],
        )
        sophia = self.create_child(
            name="Sophia",
            traits=["deep thinker", "contrarian", "empathetic"],
        )
        esther = self.create_child(
            name="Esther",
            traits=["great writer"],
        )
        jerry = self.create_child(
            name="Jerry",
            traits=["funny", "creative", "comedian", "executive assistant"],
        )
        luke = self.create_parent(
            name="Luke",
            traits=["AI project manager", "golden retriever energy"],
            max_iterations=3,
            children={0: jiang, 1: sophia, 2: esther, 42: jerry},
        )
>       luke.run(
            task="I need an executive report on Artificial General Intelligence and a list of 5 relevant jokes and quotes."
        )

agent_actors/test_system.py:125: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
agent_actors/parent.py:104: in run
    results = ray.get(tasks_completed)
../../../anaconda3/envs/py310/lib/python3.10/site-packages/ray/_private/client_mode_hook.py:105: in wrapper
    return func(*args, **kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

object_refs = [ObjectRef(359ec6ce30d3ca2d3902a25e82292a7ee269b87d0100000001000000), ObjectRef(1e8ff6d2361327843e7e1bf192eab8a5c7efcd...48dba956f36448630ff755534ffda1140100000001000000), ObjectRef(71b133a11e1c461cf36448630ff755534ffda1140100000001000000)]

    @PublicAPI
    @client_mode_hook(auto_init=True)
    def get(
        object_refs: Union[ray.ObjectRef, Sequence[ray.ObjectRef]],
        *,
        timeout: Optional[float] = None,
    ) -> Union[Any, List[Any]]:
        """Get a remote object or a list of remote objects from the object store.
    
        This method blocks until the object corresponding to the object ref is
        available in the local object store. If this object is not in the local
        object store, it will be shipped from an object store that has it (once the
        object has been created). If object_refs is a list, then the objects
        corresponding to each object in the list will be returned.
    
        Ordering for an input list of object refs is preserved for each object
        returned. That is, if an object ref to A precedes an object ref to B in the
        input list, then A will precede B in the returned list.
    
        This method will issue a warning if it's running inside async context,
        you can use ``await object_ref`` instead of ``ray.get(object_ref)``. For
        a list of object refs, you can use ``await asyncio.gather(*object_refs)``.
    
        Related patterns and anti-patterns:
    
        - :doc:`/ray-core/patterns/ray-get-loop`
        - :doc:`/ray-core/patterns/unnecessary-ray-get`
        - :doc:`/ray-core/patterns/ray-get-submission-order`
        - :doc:`/ray-core/patterns/ray-get-too-many-objects`
    
    
        Args:
            object_refs: Object ref of the object to get or a list of object refs
                to get.
            timeout (Optional[float]): The maximum amount of time in seconds to
                wait before returning. Set this to None will block until the
                corresponding object becomes available.
                WARNING: In future ray releases ``timeout=0`` will return the object
                immediately if it's available, else raise GetTimeoutError in accordance with
                the above docstring. The current behavior of blocking until objects become
                available of ``timeout=0`` is considered to be a bug, see
                https://github.com/ray-project/ray/issues/28465.
    
        Returns:
            A Python object or a list of Python objects.
    
        Raises:
            GetTimeoutError: A GetTimeoutError is raised if a timeout is set and
                the get takes longer than timeout to return.
            Exception: An exception is raised if the task that created the object
                or that created one of the objects raised an exception.
        """
        if timeout == 0:
            if os.environ.get("RAY_WARN_RAY_GET_TIMEOUT_ZERO", "1") == "1":
                import warnings
    
                warnings.warn(
                    (
                        "Please use timeout=None if you expect ray.get() to block. "
                        "Setting timeout=0 in future ray releases will raise "
                        "GetTimeoutError if the objects references are not available. "
                        "You could suppress this warning by setting "
                        "RAY_WARN_RAY_GET_TIMEOUT_ZERO=0."
                    ),
                    UserWarning,
                )
    
            # Record this usage in telemetry
            import ray._private.usage.usage_lib as usage_lib
    
            usage_lib.record_extra_usage_tag(usage_lib.TagKey.RAY_GET_TIMEOUT_ZERO, "True")
    
        worker = global_worker
        worker.check_connected()
    
        if hasattr(worker, "core_worker") and worker.core_worker.current_actor_is_asyncio():
            global blocking_get_inside_async_warned
            if not blocking_get_inside_async_warned:
                logger.warning(
                    "Using blocking ray.get inside async actor. "
                    "This blocks the event loop. Please use `await` "
                    "on object ref with asyncio.gather if you want to "
                    "yield execution to the event loop instead."
                )
                blocking_get_inside_async_warned = True
    
        with profiling.profile("ray.get"):
            is_individual_id = isinstance(object_refs, ray.ObjectRef)
            if is_individual_id:
                object_refs = [object_refs]
    
            if not isinstance(object_refs, list):
                raise ValueError(
                    "'object_refs' must either be an ObjectRef or a list of ObjectRefs."
                )
    
            # TODO(ujvl): Consider how to allow user to retrieve the ready objects.
            values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)
            for i, value in enumerate(values):
                if isinstance(value, RayError):
                    if isinstance(value, ray.exceptions.ObjectLostError):
                        worker.core_worker.dump_object_store_memory_usage()
                    if isinstance(value, RayTaskError):
>                       raise value.as_instanceof_cause()
E                       ray.exceptions.RayTaskError(TypeError): ray::AgentActor.run() (pid=28647, ip=192.168.1.71, repr=<agent_actors.actors.AgentActor object at 0x7f1a176f4fa0>)
E                         File "/home/luca/py_conda/agents/agent-actors/agent_actors/actors.py", line 12, in run
E                           return self.agent.run(*args, **kwargs)
E                         File "/home/luca/py_conda/agents/agent-actors/agent_actors/child.py", line 49, in run
E                           self.add_memory(learning)
E                         File "/home/luca/py_conda/agents/agent-actors/agent_actors/agent.py", line 150, in add_memory
E                           self.pause_to_reflect()
E                         File "/home/luca/py_conda/agents/agent-actors/agent_actors/agent.py", line 240, in pause_to_reflect
E                           insights = self._pause_to_reflect()
E                         File "/home/luca/py_conda/agents/agent-actors/agent_actors/agent.py", line 247, in _pause_to_reflect
E                           new_insights = self._synthesize_memories()
E                         File "/home/luca/py_conda/agents/agent-actors/agent_actors/agent.py", line 207, in _synthesize_memories
E                           return self.synthesis(
E                       TypeError: Chain.__call__() got an unexpected keyword argument 'context'

../../../anaconda3/envs/py310/lib/python3.10/site-packages/ray/_private/worker.py:2380: RayTaskError(TypeError)
____________________________________________________________ TestSystem.test_nested_parent_tree _____________________________________________________________

self = <agent_actors.test_system.TestSystem object at 0x7f165626b460>

    def test_nested_parent_tree(self):
        jiang = self.create_child(
            name="Jiang",
            traits=["sharp", "math, stats, and data whiz", "capitalist"],
        )
        sophia = self.create_child(
            name="Sophia",
            traits=["deep thinker", "contrarian", "empathetic"],
        )
        esther = self.create_child(
            name="Esther",
            traits=["great writer"],
        )
        luke = self.create_parent(
            name="Luke",
            traits=["AI project manager", "golden retriever energy"],
            max_iterations=3,
            children={0: jiang, 1: sophia, 2: esther},
        )
        amir = self.create_child(
            name="Amir",
            traits=["funny", "creative", "kind"],
        )
        cyrus = self.create_parent(
            "Cyrus",
            traits=["kind human"],
            max_iterations=2,
            children={0: luke, 42: amir},
        )
    
>       cyrus.run(
            task="I need an executive report on Artificial General Intelligence and a list of 5 related jokes and quotes."
        )

agent_actors/test_system.py:159: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
agent_actors/parent.py:104: in run
    results = ray.get(tasks_completed)
../../../anaconda3/envs/py310/lib/python3.10/site-packages/ray/_private/client_mode_hook.py:105: in wrapper
    return func(*args, **kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

object_refs = [ObjectRef(c76a79b2875a7251689a6d51e3a176933fee89c30100000001000000), ObjectRef(465c0fb8d6cb3cdce0ac6af40fc94d0a932242b10100000001000000)]

    @PublicAPI
    @client_mode_hook(auto_init=True)
    def get(
        object_refs: Union[ray.ObjectRef, Sequence[ray.ObjectRef]],
        *,
        timeout: Optional[float] = None,
    ) -> Union[Any, List[Any]]:
        """Get a remote object or a list of remote objects from the object store.
    
        This method blocks until the object corresponding to the object ref is
        available in the local object store. If this object is not in the local
        object store, it will be shipped from an object store that has it (once the
        object has been created). If object_refs is a list, then the objects
        corresponding to each object in the list will be returned.
    
        Ordering for an input list of object refs is preserved for each object
        returned. That is, if an object ref to A precedes an object ref to B in the
        input list, then A will precede B in the returned list.
    
        This method will issue a warning if it's running inside async context,
        you can use ``await object_ref`` instead of ``ray.get(object_ref)``. For
        a list of object refs, you can use ``await asyncio.gather(*object_refs)``.
    
        Related patterns and anti-patterns:
    
        - :doc:`/ray-core/patterns/ray-get-loop`
        - :doc:`/ray-core/patterns/unnecessary-ray-get`
        - :doc:`/ray-core/patterns/ray-get-submission-order`
        - :doc:`/ray-core/patterns/ray-get-too-many-objects`
    
    
        Args:
            object_refs: Object ref of the object to get or a list of object refs
                to get.
            timeout (Optional[float]): The maximum amount of time in seconds to
                wait before returning. Set this to None will block until the
                corresponding object becomes available.
                WARNING: In future ray releases ``timeout=0`` will return the object
                immediately if it's available, else raise GetTimeoutError in accordance with
                the above docstring. The current behavior of blocking until objects become
                available of ``timeout=0`` is considered to be a bug, see
                https://github.com/ray-project/ray/issues/28465.
    
        Returns:
            A Python object or a list of Python objects.
    
        Raises:
            GetTimeoutError: A GetTimeoutError is raised if a timeout is set and
                the get takes longer than timeout to return.
            Exception: An exception is raised if the task that created the object
                or that created one of the objects raised an exception.
        """
        if timeout == 0:
            if os.environ.get("RAY_WARN_RAY_GET_TIMEOUT_ZERO", "1") == "1":
                import warnings
    
                warnings.warn(
                    (
                        "Please use timeout=None if you expect ray.get() to block. "
                        "Setting timeout=0 in future ray releases will raise "
                        "GetTimeoutError if the objects references are not available. "
                        "You could suppress this warning by setting "
                        "RAY_WARN_RAY_GET_TIMEOUT_ZERO=0."
                    ),
                    UserWarning,
                )
    
            # Record this usage in telemetry
            import ray._private.usage.usage_lib as usage_lib
    
            usage_lib.record_extra_usage_tag(usage_lib.TagKey.RAY_GET_TIMEOUT_ZERO, "True")
    
        worker = global_worker
        worker.check_connected()
    
        if hasattr(worker, "core_worker") and worker.core_worker.current_actor_is_asyncio():
            global blocking_get_inside_async_warned
            if not blocking_get_inside_async_warned:
                logger.warning(
                    "Using blocking ray.get inside async actor. "
                    "This blocks the event loop. Please use `await` "
                    "on object ref with asyncio.gather if you want to "
                    "yield execution to the event loop instead."
                )
                blocking_get_inside_async_warned = True
    
        with profiling.profile("ray.get"):
            is_individual_id = isinstance(object_refs, ray.ObjectRef)
            if is_individual_id:
                object_refs = [object_refs]
    
            if not isinstance(object_refs, list):
                raise ValueError(
                    "'object_refs' must either be an ObjectRef or a list of ObjectRefs."
                )
    
            # TODO(ujvl): Consider how to allow user to retrieve the ready objects.
            values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)
            for i, value in enumerate(values):
                if isinstance(value, RayError):
                    if isinstance(value, ray.exceptions.ObjectLostError):
                        worker.core_worker.dump_object_store_memory_usage()
                    if isinstance(value, RayTaskError):
>                       raise value.as_instanceof_cause()
E                       ray.exceptions.RayTaskError(TypeError): ray::AgentActor.run() (pid=30284, ip=192.168.1.71, repr=<agent_actors.actors.AgentActor object at 0x7fa6d57a8eb0>)
E                         File "/home/luca/py_conda/agents/agent-actors/agent_actors/actors.py", line 12, in run
E                           return self.agent.run(*args, **kwargs)
E                         File "/home/luca/py_conda/agents/agent-actors/agent_actors/parent.py", line 104, in run
E                           results = ray.get(tasks_completed)
E                       ray.exceptions.RayTaskError(TypeError): ray::AgentActor.run() (pid=30283, ip=192.168.1.71, repr=<agent_actors.actors.AgentActor object at 0x7f959772cf70>)
E                         File "/home/luca/py_conda/agents/agent-actors/agent_actors/actors.py", line 12, in run
E                           return self.agent.run(*args, **kwargs)
E                         File "/home/luca/py_conda/agents/agent-actors/agent_actors/child.py", line 52, in run
E                           self.add_memory(learning)
E                         File "/home/luca/py_conda/agents/agent-actors/agent_actors/agent.py", line 150, in add_memory
E                           self.pause_to_reflect()
E                         File "/home/luca/py_conda/agents/agent-actors/agent_actors/agent.py", line 240, in pause_to_reflect
E                           insights = self._pause_to_reflect()
E                         File "/home/luca/py_conda/agents/agent-actors/agent_actors/agent.py", line 247, in _pause_to_reflect
E                           new_insights = self._synthesize_memories()
E                         File "/home/luca/py_conda/agents/agent-actors/agent_actors/agent.py", line 207, in _synthesize_memories
E                           return self.synthesis(
E                       TypeError: Chain.__call__() got an unexpected keyword argument 'context'

../../../anaconda3/envs/py310/lib/python3.10/site-packages/ray/_private/worker.py:2380: RayTaskError(TypeError)
_______________________________________________________ TestSystem.test_writing_a_technical_blog_post _______________________________________________________

self = <agent_actors.test_system.TestSystem object at 0x7f1656269c00>

    def test_writing_a_technical_blog_post(self):
        writer = self.create_child(
            "Writer",
            traits=[
                "brilliant technical writer",
                "programmer in a past life",
            ],
        )
        researcher = self.create_child(
            "Researcher",
            traits=[
                "expert researcher",
                "expert programmer",
            ],
        )
        cto = self.create_parent(
            "CTO",
            traits=["kind human", "expert programmer", "visionary technologist"],
            children={2: researcher, 42: writer},
        )
    
        with open("./README.md", "r") as f:
            """
            You can load your actors with memories, here, since all actors share
            the same vectorstore, this will be available to all actors.
            """
            cto.add_memory(f.read())
    
>       cto.run(
            task=f"Write a blog post about Agent Actors, a new python repository that helps you build trees of AI agents that work together to solve more complex problems. Use your memory rather than searches to learn more about Agent Actors. If you're going to search, then search for the Actor Model of Concurrency, Elixir / OTP and Plan-Do-Check-Act cycles"
        )

agent_actors/test_system.py:191: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
agent_actors/parent.py:104: in run
    results = ray.get(tasks_completed)
../../../anaconda3/envs/py310/lib/python3.10/site-packages/ray/_private/client_mode_hook.py:105: in wrapper
    return func(*args, **kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

object_refs = [ObjectRef(88543757a8df6d2fcbea5c44d6ff13ad77ef2b840100000001000000), ObjectRef(347cc60e0bb3da74e637a5084ab316bf4ef352...201e637a5084ab316bf4ef352260100000001000000), ObjectRef(c1464dc5b2308f10cbea5c44d6ff13ad77ef2b840100000001000000), ...]

    @PublicAPI
    @client_mode_hook(auto_init=True)
    def get(
        object_refs: Union[ray.ObjectRef, Sequence[ray.ObjectRef]],
        *,
        timeout: Optional[float] = None,
    ) -> Union[Any, List[Any]]:
        """Get a remote object or a list of remote objects from the object store.
    
        This method blocks until the object corresponding to the object ref is
        available in the local object store. If this object is not in the local
        object store, it will be shipped from an object store that has it (once the
        object has been created). If object_refs is a list, then the objects
        corresponding to each object in the list will be returned.
    
        Ordering for an input list of object refs is preserved for each object
        returned. That is, if an object ref to A precedes an object ref to B in the
        input list, then A will precede B in the returned list.
    
        This method will issue a warning if it's running inside async context,
        you can use ``await object_ref`` instead of ``ray.get(object_ref)``. For
        a list of object refs, you can use ``await asyncio.gather(*object_refs)``.
    
        Related patterns and anti-patterns:
    
        - :doc:`/ray-core/patterns/ray-get-loop`
        - :doc:`/ray-core/patterns/unnecessary-ray-get`
        - :doc:`/ray-core/patterns/ray-get-submission-order`
        - :doc:`/ray-core/patterns/ray-get-too-many-objects`
    
    
        Args:
            object_refs: Object ref of the object to get or a list of object refs
                to get.
            timeout (Optional[float]): The maximum amount of time in seconds to
                wait before returning. Set this to None will block until the
                corresponding object becomes available.
                WARNING: In future ray releases ``timeout=0`` will return the object
                immediately if it's available, else raise GetTimeoutError in accordance with
                the above docstring. The current behavior of blocking until objects become
                available of ``timeout=0`` is considered to be a bug, see
                https://github.com/ray-project/ray/issues/28465.
    
        Returns:
            A Python object or a list of Python objects.
    
        Raises:
            GetTimeoutError: A GetTimeoutError is raised if a timeout is set and
                the get takes longer than timeout to return.
            Exception: An exception is raised if the task that created the object
                or that created one of the objects raised an exception.
        """
        if timeout == 0:
            if os.environ.get("RAY_WARN_RAY_GET_TIMEOUT_ZERO", "1") == "1":
                import warnings
    
                warnings.warn(
                    (
                        "Please use timeout=None if you expect ray.get() to block. "
                        "Setting timeout=0 in future ray releases will raise "
                        "GetTimeoutError if the objects references are not available. "
                        "You could suppress this warning by setting "
                        "RAY_WARN_RAY_GET_TIMEOUT_ZERO=0."
                    ),
                    UserWarning,
                )
    
            # Record this usage in telemetry
            import ray._private.usage.usage_lib as usage_lib
    
            usage_lib.record_extra_usage_tag(usage_lib.TagKey.RAY_GET_TIMEOUT_ZERO, "True")
    
        worker = global_worker
        worker.check_connected()
    
        if hasattr(worker, "core_worker") and worker.core_worker.current_actor_is_asyncio():
            global blocking_get_inside_async_warned
            if not blocking_get_inside_async_warned:
                logger.warning(
                    "Using blocking ray.get inside async actor. "
                    "This blocks the event loop. Please use `await` "
                    "on object ref with asyncio.gather if you want to "
                    "yield execution to the event loop instead."
                )
                blocking_get_inside_async_warned = True
    
        with profiling.profile("ray.get"):
            is_individual_id = isinstance(object_refs, ray.ObjectRef)
            if is_individual_id:
                object_refs = [object_refs]
    
            if not isinstance(object_refs, list):
                raise ValueError(
                    "'object_refs' must either be an ObjectRef or a list of ObjectRefs."
                )
    
            # TODO(ujvl): Consider how to allow user to retrieve the ready objects.
            values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)
            for i, value in enumerate(values):
                if isinstance(value, RayError):
                    if isinstance(value, ray.exceptions.ObjectLostError):
                        worker.core_worker.dump_object_store_memory_usage()
                    if isinstance(value, RayTaskError):
>                       raise value.as_instanceof_cause()
E                       ray.exceptions.RayTaskError(InvalidRequestError): ray::AgentActor.run() (pid=32550, ip=192.168.1.71, repr=<agent_actors.actors.AgentActor object at 0x7f0f8a934f10>)
E                         File "/home/luca/py_conda/agents/agent-actors/agent_actors/actors.py", line 12, in run
E                           return self.agent.run(*args, **kwargs)
E                         File "/home/luca/py_conda/agents/agent-actors/agent_actors/child.py", line 42, in run
E                           result = self.do(
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/langchain/chains/base.py", line 116, in __call__
E                           raise e
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/langchain/chains/base.py", line 113, in __call__
E                           outputs = self._call(inputs)
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/langchain/agents/agent.py", line 807, in _call
E                           output = self.agent.return_stopped_response(
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/langchain/agents/agent.py", line 515, in return_stopped_response
E                           full_output = self.llm_chain.predict(**full_inputs)
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/langchain/chains/llm.py", line 151, in predict
E                           return self(kwargs)[self.output_key]
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/langchain/chains/base.py", line 116, in __call__
E                           raise e
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/langchain/chains/base.py", line 113, in __call__
E                           outputs = self._call(inputs)
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/langchain/chains/llm.py", line 57, in _call
E                           return self.apply([inputs])[0]
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/langchain/chains/llm.py", line 118, in apply
E                           response = self.generate(input_list)
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/langchain/chains/llm.py", line 62, in generate
E                           return self.llm.generate_prompt(prompts, stop)
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/langchain/chat_models/base.py", line 82, in generate_prompt
E                           raise e
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/langchain/chat_models/base.py", line 79, in generate_prompt
E                           output = self.generate(prompt_messages, stop=stop)
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/langchain/chat_models/base.py", line 54, in generate
E                           results = [self._generate(m, stop=stop) for m in messages]
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/langchain/chat_models/base.py", line 54, in <listcomp>
E                           results = [self._generate(m, stop=stop) for m in messages]
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/langchain/chat_models/openai.py", line 266, in _generate
E                           response = self.completion_with_retry(messages=message_dicts, **params)
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/langchain/chat_models/openai.py", line 228, in completion_with_retry
E                           return _completion_with_retry(**kwargs)
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/tenacity/__init__.py", line 289, in wrapped_f
E                           return self(f, *args, **kw)
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/tenacity/__init__.py", line 379, in __call__
E                           do = self.iter(retry_state=retry_state)
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/tenacity/__init__.py", line 314, in iter
E                           return fut.result()
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/concurrent/futures/_base.py", line 451, in result
E                           return self.__get_result()
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result
E                           raise self._exception
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/tenacity/__init__.py", line 382, in __call__
E                           result = fn(*args, **kwargs)
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/langchain/chat_models/openai.py", line 226, in _completion_with_retry
E                           return self.client.create(**kwargs)
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/openai/api_resources/chat_completion.py", line 25, in create
E                           return super().create(*args, **kwargs)
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/openai/api_resources/abstract/engine_api_resource.py", line 153, in create
E                           response, _, api_key = requestor.request(
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/openai/api_requestor.py", line 226, in request
E                           resp, got_stream = self._interpret_response(result, stream)
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/openai/api_requestor.py", line 620, in _interpret_response
E                           self._interpret_response_line(
E                         File "/home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/openai/api_requestor.py", line 683, in _interpret_response_line
E                           raise self.handle_error_response(
E                       openai.error.InvalidRequestError: This model's maximum context length is 4097 tokens. However, you requested 4136 tokens (3112 in the messages, 1024 in the completion). Please reduce the length of the messages or completion.

../../../anaconda3/envs/py310/lib/python3.10/site-packages/ray/_private/worker.py:2380: RayTaskError(InvalidRequestError)
===================================================================== warnings summary ======================================================================
../../../anaconda3/envs/py310/lib/python3.10/site-packages/langchain/output_parsers/pydantic.py:20
  /home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/langchain/output_parsers/pydantic.py:20: DeprecationWarning: invalid escape sequence '\{'
    "\{.*\}", text.strip(), re.MULTILINE | re.IGNORECASE | re.DOTALL

../../../anaconda3/envs/py310/lib/python3.10/site-packages/langchain/output_parsers/format_instructions.py:3
  /home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/langchain/output_parsers/format_instructions.py:3: DeprecationWarning: invalid escape sequence '\`'
    STRUCTURED_FORMAT_INSTRUCTIONS = """The output should be a markdown code snippet formatted in the following schema, including the leading and trailing "\`\`\`json" and "\`\`\`":

../../../anaconda3/envs/py310/lib/python3.10/site-packages/faiss/loader.py:28
  /home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/faiss/loader.py:28: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
    if LooseVersion(numpy.__version__) >= "1.19":

../../../anaconda3/envs/py310/lib/python3.10/site-packages/setuptools/_distutils/version.py:345
  /home/luca/anaconda3/envs/py310/lib/python3.10/site-packages/setuptools/_distutils/version.py:345: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
    other = LooseVersion(other)

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
================================================================== short test summary info ==================================================================
FAILED agent_actors/test_system.py::TestSystem::test_parent_overhead - TypeError: Chain.__call__() got an unexpected keyword argument 'context'
FAILED agent_actors/test_system.py::TestSystem::test_parallel_map_reduce - ray.exceptions.RayTaskError(TypeError): ray::AgentActor.run() (pid=28647, ip=192.168.1.71, repr=<agent_actors.actors.AgentActor object at 0x7f1a176f4fa0>)
FAILED agent_actors/test_system.py::TestSystem::test_nested_parent_tree - ray.exceptions.RayTaskError(TypeError): ray::AgentActor.run() (pid=30284, ip=192.168.1.71, repr=<agent_actors.actors.AgentActor object at 0x7fa6d57a8eb0>)
FAILED agent_actors/test_system.py::TestSystem::test_writing_a_technical_blog_post - ray.exceptions.RayTaskError(InvalidRequestError): ray::AgentActor.run() (pid=32550, ip=192.168.1.71, repr=<agent_actors.actors.AgentActor object at 0x7f0f8a934f10>)